Biology subjects

Samaras, D.

Publications and source records attributed to Samaras, D..

3 recordsLinked to original sources

bioRxiv2025

CILIATED CELL DOMAINS WITH LOCALLY COORDINATED CILIARY MOTION GENERATE A MOSAIC OF MICROFLOWS IN THE BRAIN'S LATERAL VENTRICLES

Circulation of cerebrospinal fluid (CSF) through the brains ventricles is essential for maintaining brain homeostasis and supporting neurogenesis. CSF flow is supported by the structural polarization of multiciliated cells, which align with the flow direction. However, it remains unclear how the organization of tissue-wide polarity across the ciliary epithelium comprised of thousands of cells, determines the trajectory of the flow and efficient distribution of the CSF. Here, we used new approaches to analyze the organization of translational polarity across extensive areas of the lateral ventricular wall. We also used live imaging to examine cilia motion, flow trajectories, and ciliary beat frequency (CBF) in live preparations of ventricles. In addition to the primary flow running across the ventricular wall from the posterior area to the anterior (P-A), we found multiple local microflows with both direct and curved trajectories that deviate from the mainstream P-A direction. Our results suggest that the ciliated epithelium in the lateral ventricles varies in the alignment of ciliated cell translational polarity: whereas in the narrow dorsal area translational polarity is aligned with the direction of the mainstream flow, in the periphery of the mainstream it is organized into distinct cell clusters with locally aligned polarity vectors. We posit that the cluster organization of the multiciliated ependymal cells underpins the generation of a complex mosaic of flows, with the local microflows facilitating the wide spreading of the CSF across the ependyma. We demonstrate that nNOS is involved in the control of translational polarity, cluster organization, microflows, and CBF in the ependyma. SignificanceThe flow of cerebrospinal fluid (CSF) is crucial for brain homeostasis. This flow is driven by the coordinated beating of cilia on thousands of ciliated cells. Planar polarity vector of the ciliated cells is aligned with the flow direction in the areas of the ependyma underlying the mainstream flow (1). However, the overall alignment of planar cell polarity and flow across the whole tissue is unclear. Here we report a discovery of complex flow patterns over the ependyma, consisting of numerous microflows in the periphery of the mainstream flow. Using new approaches, we found that planar polarity of the ependymal ciliated cells on the periphery of the mainstream flow aligns only locally, indicating a clustered organization of the ependyma that supports various flow directions.

cell biology↗

bioRxiv2020

COCO-Search18: A Dataset for Predicting Goal-directed Attention Control

Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control--saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding[~] 300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a persons intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.

animal behavior and cognition↗

bioRxiv2018

Modeling Attention Control Using A Convolutional Neural Network Designed After The Ventral Visual Pathway

Recently we proposed that people represent object categories using category-consistent features (CCFs), those features that occur both frequently and consistently across a categorys exemplars [70]. Here we designed a Convolutional Neural Network (CNN) after the primate ventral stream (VsNet) and used it to extract CCFs from 68 categories of objects spanning a three-level category hierarchy. We evaluated VsNet against people searching for the same targets from the same 68 categories. Not only did VsNet replicate our previous report of stronger attention guidance to subordinate-level targets, with its more powerful CNN-CCFs it was able to predict attention control to individual target categories-the more CNN-CCFs extracted for a category, the faster gaze was directed to the target. We also probed VsNet to determine where in its network of layers these attention control signals originate. We found that CCFs extracted from VsNets V1 layer contributed most to guiding attention to targets cued at the subordinate (e.g., police car) and basic (e.g., car) levels, but that guidance to superordinate-cued (e.g., vehicle) targets was strongest using CCFs from the CIT+AIT layer. We also identified the image patches eliciting the strongest filter responses from areas V4 and higher and found that they depicted representative parts of an object category (e.g., advertisements appearing on top of taxi cabs). Finally, we found that VsNet better predicted attention control than comparable CNN models, despite having fewer convolutional filters. This work shows that a brain-inspired CNN can predict goal-directed attention control by extracting and using category-consistent features.

neuroscience↗